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Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data

Quan, Hude MD, PhD*†; Sundararajan, Vijaya MD, MPH, FACP‡; Halfon, Patricia MD§; Fong, Andrew BCOMM*; Burnand, Bernard MD, MPH§; Luthi, Jean-Christophe MD, PhD§; Saunders, L Duncan MBBCh, PhD¶; Beck, Cynthia A. MD, MASc*∥; Feasby, Thomas E. MD**; Ghali, William A. MD, MPH*†††

Original Article

Objectives: Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms.

Methods: ICD-10 coding algorithms were developed by “translation” of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians’ assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms.

Results: Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm.

Conclusions: These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.

From the *Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada; †Centre for Health and Policy Studies, University of Calgary, Calgary, Alberta, Canada; ‡Victorian Department of Human Services, Australia; §Health Care Evaluation Unit, Institut Universitaire de Médecine Sociale et Préventive, University of Lausanne, Switzerland; ¶Department of Public Health Sciences, University of Alberta, Edmonton, Alberta, Canada; ∥Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada; **Department of Medicine, University of Alberta, Edmonton, Alberta, Canada; and ††Department of Medicine, University of Calgary, Calgary, Alberta, Canada.

Supported by an operating grant from the Canadian Institutes of Health Research, Canada. Dr. Quan is supported by a Population Health Investigator Award from the Alberta Heritage Foundation for Medical Research, Edmonton, Alberta, Canada, and by a New Investigator Award from the Canadian Institutes of Health Research. Dr. Ghali is supported by a Health Scholar Award from the Alberta Heritage Foundation for Medical Research, Edmonton, Alberta, Canada, and by a Government of Canada Chair in Health Services Research. Dr. Beck is supported by a Clinical Fellowship from the Alberta Heritage Foundation for Medical Research, Edmonton, Alberta, Canada.

Reprints: Dr. Hude Quan, Department of Community Health Sciences, University of Calgary, 3330 Hospital, Dr. NW, Calgary, Alberta, Canada, T2N 4N1. E-mail: hquan@ucalgary.ca.

© 2005 Lippincott Williams & Wilkins, Inc.